In the past few years, significant progress has been made on deep neural networks (DNNs)
in achieving human-level performance on several long-standing tasks. With the broader …

Great advances in deep neural networks (DNNs) have led to state-of-the-art performance on
a wide range of tasks. However, recent studies have shown that DNNs are vulnerable to …

Deep learning methods are widely regarded as indispensable when it comes to designing
perception pipelines for autonomous agents such as robots, drones or automated vehicles …

Deep neural networks have achieved impressive experimental results in image
classification, but can surprisingly be unstable with respect to adversarial perturbations, that …

Neural networks have been widely applied in security applications such as spam and
phishing detection, intrusion prevention, and malware detection. This black-box method …

Despite the functional success of deep Neural Networks, their trustworthiness remains a
crucial open challenge. To address this challenge, both testing and verification techniques …

Ensuring robustness of Deep Neural Networks (DNNs) is crucial to their adoption in safety-
critical applications such as self-driving cars, drones, and healthcare. Notably, DNNs are …

We propose a novel confidence metric, namely, attribution-based confidence (ABC) for deep
neural networks (DNNs). ABC metric characterizes whether the output of a DNN on an input …

Deep neural networks (DNNs) have transformed several artificial intelligence research
areas including computer vision, speech recognition, and natural language processing …

Deep neural networks (DNNs) have become one of the enabling technologies in many
safety-critical applications, eg, autonomous driving and medical image analysis. DNN …